Among internet advertisers, behavioral targeting (BT) is a common way to target internet advertisements towards a segment of the internet audience. BT algorithms attempt to match users to ads based on the historical activity of the users and the perceived category of the advertisement. For example, a user who had browsed pages (e.g. web pages) related to automobiles yesterday might be a good candidate for being presented an auto-related advertisement today. Although there are many kinds of historical user features that are useful in BT, the state of the art is advanced by focusing on a class of features shown herein to be a good indicator of user interest, namely search queries.
The very nature of the internet facilitates a two-way flow of information between users and advertisers and allows these transactions to be conducted in real time or near-to-real time. For example, a user may request an ad and may intentionally, or inherently, transmit various pieces of data describing himself or herself. Additionally, an advertising management system may be able to intelligently determine which ads to place on a given web page at a given website property requesting advertisement content, thus increasing the revenue for the parties involved and increasing user satisfaction by eliminating “nuisance” ads.
Current systems, including BT systems, fail to fully exploit the interactive aspects of the internet in the advertising realm. In some cases, current advertising systems do not take full advantage of the stores of information available allocating advertisements to advertisement placements. For example, current BT systems fail to provide “cross-category” associations for queries. In current BT implementations, an automatic query categorizer is used to assign categories to queries, yet only “in-category” queries are used as evidence to qualify a user as having interest in such a category.
However, there may be certain queries (and associated advertisements) that are associated with a BT category (i.e. a cross-category), but would not be categorized into that category using current BT systems. For example, a query like “cash for clunkers” may be categorized into the “Finance” category by a content-based query categorizer, but it may be even more strongly associated with clicks in the “Autos” category.
Accordingly, there exists a need for predicting the cross-category search queries, and using the predicted cross-category search queries for optimization of allocation of advertisements to a user in a network-based environment.